Iris data extraction

ABSTRACT

A process for extracting iris data for biometric identification includes a thresholding method where the thresholds are selected according to a nonparametric approach that considers the grey scale and does not require classifying pixels as edge or non-edge pixels. An eye image is first acquired, where the eye image has component images including an iris image with an inner boundary and an outer boundary. The eye image has a distribution of grey levels. Component images, such as an iris image or a pupil image, from the eye image are segmented according to the distribution of grey levels. The inner boundary and outer boundary of the iris image are determined from the component images. The iris image within the inner boundary and outer boundary is processed for biometric identification. The component images may be segmented by creating an eye histogram of pixel intensities from the distribution of grey levels.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of priorityunder 35 U.S.C. §120 to U.S. application Ser. No. 13/723,711, filed Dec.21, 2012, now abandoned, which is a continuation of U.S. applicationSer. No. 13/096,401, filed Apr. 28, 2011, which issued as U.S. Pat. No.8,340,364, on Dec. 25, 2012, which is a division of U.S. applicationSer. No. 11/526,096, filed Sep. 25, 2006, which issued as U.S. Pat. No.7,970,179 on Jun. 28, 2011, the entire contents of all of which arehereby fully incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to biometric identification using an irisimage, and more particularly, to identification of the iris image in aneye image for extracting iris data.

2. Description of Related Art

Due to the unique character of each individual's iris, various systemsattempt to use the iris for biometric identification. Such systemsgenerally capture an image of the entire eye, which includes an image ofthe iris. The iris image must be identified before the patterns of theiris, which are unique to each individual, can be extracted forbiometric analysis. In other words, the area that corresponds to theiris must be segmented, or separated, from the other components in theentire eye image. Conventional systems generally determine theboundaries of the iris image by searching for the edges that correspondwith these boundaries. In particular, these conventional approachesdepend on the contrast between the edges and the area around the edges,referred to as edge strength, to identify the boundaries. As describedfurther below, this approach suffers from many disadvantages and failsto provide a robust method for finding and extracting iris data. Forinstance, because the edges between the iris and the sclera (limbicboundary) are often weak and hard to detect, conventional systems arebeset with the difficult challenge of enhancing the weak edges toidentify the iris boundaries adequately.

U.S. Pat. No. 5,291,560 to Daugman implements an integro-differentialoperator for locating the circular edges of the iris and pupil regions,as well as edges of the arcs of the upper and lower eyelids. As a firststep, Daugman approximates the edge of the pupil to be a circle and sumsthe brightness along each circle with trial center coordinates (x₀, y₀)and incrementally increasing radius r. When radius r reaches the edge ofthe pupil, there is a sudden change in the brightness since thebrightness of pixels in the pupil region should be different from pixelsin the iris region just outside the pupil region. Various trial centercoordinates (x₀, y₀) can be evaluated to find the center of the pupil.If the center coordinates (x₀, y₀) do not coincide with the pupil'scenter, some portions of the circle will still lie within the pupilregion when the brightness suddenly changes. However, if the centercoordinates (x₀, y₀) do indeed coincide with the pupil's center, whenthe brightness suddenly changes, no portions of the circle should be inthe pupil region, so the rate-of-change of the brightness, or luminance,should be at its maximum. Thus, the problem of locating the pupil'sboundary is reduced to an optimization problem where a three-parameterspace is searched for the best combination of center coordinates (x₀,y₀) and radius r, i.e., where the absolute value of the partialderivative with respect to radius r of the integrated luminance alongthe circle is maximum. The search of the three-parameter space can occurin an iterative process of gradient-ascent.

As a second step, Daugman's approach for finding the outer edge of theiris, also known as the limbic boundary, is similar to finding the edgeof the pupil but the previous approach is modified to account for thefact that i) the pupil is not always centered in the iris, ii) the upperand lower eyelids obscure top and bottom portions of the iris, and iii)the iris, unlike the pupil, has a concentric texture and may itselfcontain interior circular edges which could create sudden changes inintegrated luminance along a circle. The process for detecting the irisedge therefore is restricted to two 45-degree arcs along the horizontalmeridian and an area integral is used rather than a contour integral.The luminance for arcs of increasing radius and centered at the pupilcenter are evaluated as an area integral in polar coordinates. The valueof radius r which corresponds to the maximum in the rate-of-change ofintegrated luminance with respect to radius r corresponds to an edge ofthe iris. This calculation is made for each arc separately since theleft and right edges of the iris may be at different distances, i.e.radius r, from the pupil's center.

Disadvantageously, as Daugman acknowledges by using area integrals whenprocessing the iris image, the algorithm can fail because it issusceptible to changes in luminance that do not occur at the boundaries,such as those caused by reflections, noise from a poor image, contactlens edges, or even by actual features or textures in the eye. Inparticular, the use of integro-differential operators are sensitive tothe specular spot reflection of non-diffused artificial light that canoccur inside the pupil, and such spots can cause the detection of thecorrect inner boundary to fail. Therefore, Daugman has also proposed theuse of Gaussian filtering to smooth the texture patterns inside the irisregion to avoid incorrect detection of false limbic boundaries, but thisapproach involves heavy computational complexity.

In addition, the process above must be modified to account for the factthat the edges are not clean edges and are somewhat fuzzy. Theintegrated luminance of a shell sum must be used rather than theintegrated luminance of the circle, i.e. the rate-of-change of a shellsum is maximized. Therefore, use of the Daugman may require ad hocadjustments of the shell size parameter.

U.S. Pat. Nos. 5,751,836 and 5,752,596 to Wildes et al. also implementedge detection algorithms. The process disclosed by Wildes et al.initially averages and reduces the input image using a low-pass Gaussianfilter that spatially averages and reduces high frequency noise. Theresult is then subsampled without further loss of information but withthe advantage of reducing computational demands. The iris is thenlocalized by locating the limbic (outer) boundary of the iris, thepupillary (inner) boundary of the iris, and the eyelid boundaries. Theiris is then taken as the portion of the image that is outside thepupillary boundary, inside the limbic boundary, above the lower eyelid,and below the upper eyelid.

The first step in locating each component of the iris boundary employs agradient-based edge detection operation which forms an edge map bycalculating the first derivatives of intensity values and thenthresholding the result. Wildes et al. bias the derivatives in thehorizontal direction for detecting the eyelids, and in the verticaldirection for detecting the limbic boundary. The application of theprocess taught by Wildes et al. has the disadvantage of requiringthreshold values to be chosen for edge detection. Poor choice ofthreshold values can eliminate critical edge points possibly causingfailure in the detection of the circles and arcs making up theboundaries of the iris.

Using the resulting edge map, the second step employs a transform likethat generally disclosed in U.S. Pat. No. 3,069,654 to Hough. The limbicboundary is modeled as a circle with center coordinates (x₀, y₀) andradius r. Thus, the detected edge pixels from the edge map are thinnedto increase the number of meaningful edges. The pixels are thenhistogrammed into a three-dimensional space formed by circle parametersx₀, y₀, and r (Hough circle transform). The (x₀, y₀, r) point with themost number of votes from the histogramming process then represents thelimbic boundary. Similarly, the pupil is also modeled as a circle andthe edge pixels are thinned and histogrammed into (x₀, y₀, r) values,where the (x₀, y₀, r) point with the most votes are taken to representthe pupillary boundary. The eyelid boundaries, however, are modeled astwo separate parabolic arcs. The eyelid edges are thinned andhistogrammed according to the parameters necessary to define a parabolicarc (parabolic Hough transform), where the set of parameters with themost votes is taken to represent the upper or lower eyelids.

In the article titled “Recognition of Human Iris Patterns for BiometricIdentification” by Libor Masek, the Hough circle transform is also used,but unlike Wildes et al., Masek first employs Canny edge detection tocreate the edge map. Masek modifies Kovesi's Canny edge detection toallow for the weighting of gradients as Wildes et al. teaches for thedetection of the limbic boundary with a vertical bias. The Hough circletransform is applied to find the limbic boundary first and then thepupillary boundary where the range of radius values for the search isset manually depending on the database used and the typical radiusvalues in the images.

With respect to the eyelids, Masek applies Canny edge detection whereonly horizontal gradient information is taken. The eyelids are thenlocated by fitting a line to the upper and lower eyelids through alinear Hough transform. A second horizontal line is formed from theintersection of the fitted line and the limbic boundary closest to thepupil in order to achieve maximum isolation of the eyelids. The use ofthe linear Hough transform is less computationally demanding than theuse of the parabolic Hough transforms taught by Wildes et al. However,maximum isolation of the eyelid regions can isolate substantial portionsof the iris itself and make the matching process less accurate.

Disadvantageously, Masek requires threshold values to be specified tocreate the edge maps, and as with Wildes et al., these threshold valuesare dependent on the database and the quality of images in the database.Poor choice of threshold values can eliminate critical edge pointspossibly causing failure in the detection of the circles and arcs makingup the boundaries of the iris.

In “Experiments with an Improved Iris Segmentation Algorithm” (FourthIEEE Workshop on Automatic Identification Advanced Technologies(AutoID), October 2005, New York), Lui et al. disclose a process knownas ND_IRIS, which attempts to improve Masek's approach towardsegmentation. Masek's method detects the outer iris boundary first andthen detects the inner boundary within the outer boundary, but Lui etal. reverses this order by detecting the inner boundary first sincethere is often greater contrast between the pupil and iris, and thus theinner boundary can be easier to localize. Lui at al. also point out thatedge pixels that do not lie on the iris boundary often cause the Houghtransform to find an incorrect boundary. Thus, edges within the pupiland the iris are further reduced through thresholding before Canny edgedetection. In addition, Lui et al. modifies the Hough transform andimplements a verification step which compares the areas on both sides ofa proposed boundary. Also, each eyelid is modeled as two straight linesrather than just one. Despite improved iris segmentation, ND_IRIS, likethe Masek approach, fails particularly when the image quality is low.Published results also suggest that ND_IRIS problematically has higherrecognition rates for light iris images than for dark iris images.

The conventional processes above are not robust because they all requiresearching for parameters that define the shapes that approximate theiris boundaries. Daugman searches for the best combination of centercoordinates (x₀, y₀) and radius r to define a pupil circle and arcs thatmark the left and right edges of the iris. Meanwhile, Wildes et al.applies the circle and parabolic Hough transforms which creates ahistogram in to a parametric-space, e.g. a three-dimensional (x₀, y₀, r)space for the circle Hough transform, to define circular and parabolicshaped boundaries of the iris. Similarly, Masek applies circle andlinear Hough transforms.

In addition, approaches that rely on edge strength to detect eyecomponents may require the use of a edge strength threshold. Generally,the edge strength threshold must be determined adaptively at a highcomputational cost. If the selected edge strength is too high, only theedges from the eyelids and eyelashes can be identified. On the otherhand, if the selected edge strength is too low, too many edges areidentified for analysis. As a result, a fourth parameter correspondingto the threshold for edge strength, in addition to x, y positions andradii, must be searched. The edge strength threshold is determinediteratively by processing the database of captured images. If the camerasettings or lighting conditions change, however, a new edge strengththreshold must be recalculated by iteratively processing the database ofimages captured under the new camera settings or lighting conditions. Assuch, the need to recalculate parameters every time conditions changemakes the conventional edge-detection systems above less robust.

In addition, the processes above can be highly dependent on the qualityof the images being analyzed. For instance, Daugman requires a shellsize parameter to be adjusted while Wildes et al. and Masek requirethreshold values to be chosen for edge detection.

Furthermore, because techniques, such as those taught by Daugman andWildes et al., identify edges by analyzing localized areas of the imagein an incremental manner, the techniques are computationally costly ifthe approximate location of the iris is not known. As such, a fullsearch along the x- and y-axes as well as the radii for the entire imagemay be required.

In “Efficient iris recognition by characterizing key local variation”(IEEE Transactions on Image Procession, vol. 13, no. 6, 739-50, 2004),Ma et al. attempt to lower the computational cost by using a “global”projection along the x- and y-axes to find the minima position as theapproximate position of the iris. A narrower region, e.g. 120×120,centered on the point is then binarized to a reasonable threshold usingthe grey level histogram twice to find the center. The lighting of theiris, however, usually creates a bright spot of spots inside the pupil,and results in the minima of the xy projection to be at the wronglocation.

Despite the disadvantages of the approaches described above, currentattempts to improve segmentation continue to focus exclusively onanalyzing edges particularly through parametric approaches, namely themethods used by Daugman and Masek. As described above, Lui et al. uses asegmentation approach based on Masek. In addition, in the article“Person identification technique using human iris recognition,” Tisse etal. implement an improved system based on the integro-differentialoperators as taught by Daugman in combination with a Hough transform.Moreover, in “A Fast Circular Edge Detector for the Iris RegionSegmentation” (Biolgically Motivated Computer Visio: First IEEEInternational Workshop, Seoul, Korea, May 15-17, 2000), Park et al.propose using a method based on Daugman where the need for Gaussianfiltering proposed by Daugman is eliminated by searching from a radius rthat is independent of the texture patterns in the iris to find thelimbic boundary.

SUMMARY OF THE INVENTION

To avoid the problems of the conventional edge-detection approachesdescribed above, the present invention implements a thresholding methodwhere the thresholds are selected according to a nonparametric approachthat considers the grey scale and does not require classifying pixels asedge or non-edge pixels.

Accordingly, an embodiment of the present invention provides a methodfor extracting iris data from an eye for biometric identification. Aneye image is first acquired, where the eye image has a plurality ofcomponent images including an iris image with an inner boundary and anouter boundary. Moreover, the eye image has a distribution of greylevels. Component images, such as an iris image or a pupil image, fromthe eye image are segmented according to the distribution of greylevels. The inner boundary and outer boundary of the iris image aredetermined from the component images. The iris image within the innerboundary and outer boundary are then processed.

In particular, the component images may be segmented by creating an eyehistogram of pixel intensities from the distribution of grey levels ofthe eye image, where the eye histogram has classes, each of whichcorresponds to one of the component images. Thresholds in the eyehistogram are selected to divide the classes of the eye histogram. Thethresholds may be selected by maximizing between-class variances, whichmay include retrieving results for pre-computed arithmetic calculationsfrom a look-up table. Thresholded images corresponding to each of theclasses are then created.

These and other aspects of the present invention will become moreapparent from the following detailed description of the preferredembodiments of the present invention when viewed in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the steps in an exemplary method for applyingmulti-level thresholding to determine the size and position of the irisimage to enable extraction of iris data.

FIG. 2 illustrates an example of a captured eye image.

FIG. 3 illustrates an exemplary method for applying multi-levelthresholding to create thresholded images of components in the eyeimage.

FIG. 4 illustrates an example of an eye histogram.

FIG. 5A illustrates an example of a digitally captured eye image.

FIG. 5B illustrates the eye image of FIG. 5A after the application of amax-filter.

FIG. 5C illustrates the thresholded pupil image after multi-levelthresholding has been executed for the eye image of FIG. 5B.

FIG. 5D illustrates the thresholded iris image after the multi-levelthresholding has been executed for the eye image of FIG. 5B.

FIG. 6 illustrates an exemplary method for correcting unevenillumination in the eye image.

DETAILED DESCRIPTION

The present invention implements a thresholding method where thethresholds are selected according to a nonparametric approach thatconsiders the grey scale and does not require classifying pixels as edgeor non-edge pixels.

Unlike systems relying on an edge strength threshold to identify eyecomponents in an image, the present invention avoids the need to processiteratively all images in the database for each set of image captureconditions in order to determine an edge strength threshold. In oneaspect of the present invention, the grey scale for each individualimage is sufficient to identify the iris in the particular image. Thereis no need to process a series of captured images to preselect edgestrength thresholds required to find the edges in the conventionaledge-detection systems. Moreover, the conditions for image capturing donot need to remain static.

In addition, unlike the “local” edge based methods of the iris employedby the conventional approaches, the present invention considersinformation from the entire image in a “global” manner. In other words,the present invention uses a grey scale histogram of the entire image,rather than information about edges in a localized area of the image. Asa result, the location of the pupil and iris is identified at once.

It has been discovered that the iris image in a captured eye image canbe identified by employing a modified version of Otsu's thresholdselection method conceived by Liao et al. In “A Threshold SelectionMethod from Grey-Level Histograms” (IEEE Transactions on Systems, Man,and Cybernetics, vol. SMC-9, No. 1, January 1979), Otsu uses anon-parametric and unsupervised method of automatic threshold selectionfor picture segmentation. An optimal threshold or set of thresholds isselected from a grey level histogram using discriminant analysis, i.e.by maximizing the between-class variance. Otsu's method is consideredone of the better threshold selection methods for general real worldimages with regard to uniformity and shape measures. However, Otsurequires an exhaustive search to evaluate the criterion for maximizingthe between-class variance, and the method requires too much time to bepractical for selecting multiple threshold levels. Thus, in “A FastAlgorithm for Multilevel Thresholding” (Journal of Information Scienceand Engineering 17, 713-727 (2001)), Liao et al. propose a faster, moreefficient version of Otsu's method. In particular, Liao et al. proposesa criterion for maximizing a modified between-class variance that isequivalent to the criterion of maximizing the between-class variancetaught by Otsu's method. Using the new criterion of Liao et al., arecursive algorithm is then employed to efficiently find the optimalthreshold. The modified between-class variance can be pre-computed andstored in a lookup table. Thus, the method of Liao et al. is moreefficient because the new criterion requires fewer computations and thelookup table eliminates the need to repeat arithmetic operations.

Accordingly, FIG. 1 illustrates an exemplary embodiment 100 of thepresent invention which employs a thresholding technique, such as thattaught by Liao et al. Initially, an eye image 102, particularly adigital image, is acquired. Preferably, the image of the eye is takenunder near-infrared light in order to enhance the pattern of the iris.The eye image can be obtained by various techniques known to those ofskill in the art, and thus, the details are omitted. In general, the eyeimage should have an even contrast across the entire eye.

As shown in FIG. 2, the eye image 200 has a plurality of componentimages including a pupil image 202, an iris image 206, an eyelids image212, and an eyelashes image 214. The pupil image 202 has a boundary 204,while the iris image 206 has an inner boundary 208 and outer boundary210. In general, the outer boundary 204 of the pupil image coincideswith the inner boundary 208 of the iris image 206. Moreover, as furtherillustrated in FIG. 2, the eye has a distribution of grey levels.

In step 104, an optional filter may be applied to the captured eye image200. In particular, a conventional max-filter, which reduces peppernoise, can be applied to the image to lessen the effect of the eyelashesimage 214, which are generally thin and darker than the eyelids. FIG. 5Ashows an example of a digitally captured eye image, and FIG. 5B showsthe image of FIG. 5A after the application of a max-filter. However, asdescribed below, the present invention, when compared to conventionalapproaches, is less sensitive to noise, such as noise created byeyelashes.

In step 106, the component images of the eye image are identifiedaccording to the distribution of grey levels in the process known asmulti-level thresholding. FIG. 3 illustrates an exemplary embodiment 300of the multi-level thresholding of step 106. In particular, Liao etal.'s modified version of Otsu's method may be applied to process thepixels, or basic units, of the eye image and determine multiplethreshold levels. In step 302, an eye histogram 304 of pixel intensitiesis created from the distribution of grey levels in the eye image. Theeye histogram has classes that each correspond to one of the componentimages in the eye image. In step 306, the modified between-classvariance as taught by Liao et al. can be pre-computed and stored in alookup table 308 to reduce computational expense. In step 310, themodified between-class variance is maximized, and in step 312, thethresholds 314 corresponding to the classes of the eye histogram 304 aredetermined. In particular, a recursive algorithm as taught by Liao etal. is employed to efficiently find the optimal thresholds 312. Thus,components of the eye image are identified according to the calculatedthresholds 314. Preferably, at least three threshold levels arecalculated in order to identify the pupil and iris components of the eyeimage, but additional threshold levels may be employed to provide morerefined segmentation and to further eliminate parts of the eye imagethat do not correspond with the pupil and iris. In particular, theexemplary embodiment of the present invention described herein employsfour thresholds. Once the threshold levels identify the pupil and iriscomponents of the eye image, the remaining components of the image canbe discarded.

As illustrated in FIG. 4, the example histogram 400 for an eye imagereveals several peaks. The iris image generally has higher grey valuesthan the pupil image, and the sclera image generally has higher greyvalues than the iris image. Thus, referring to FIG. 4, the first peak402 corresponds to the pupil, the second peak 404 corresponds to theiris, and subsequent peaks 406 correspond to the sclera and other partsof the eye image. The selected thresholds 312 correspond to theseidentifiable peaks in the histogram 304.

As further shown in FIG. 3, a thresholded image corresponding to each ofthe classes, or component images, is then created in step 314. Inparticular, as shown in FIG. 1, step 106 produces a thresholded pupilimage 108 and a thresholded iris image 130 corresponding to the pupilimage and the iris image, respectively. FIGS. 5C and 5D illustrate thethresholded pupil and iris images, respectively, after the multi-levelthresholding method of step 106 is executed. The thresholded pupil imageis used to find the inner (pupillary) boundary of the iris image. Withthe further detection of the outer edge from the thresholded iris image130, the inner and the outer boundaries indicate the region from whichiris biometric data can be obtained.

The present invention is robust because it is not necessary to searchfor parameters as required in the conventional approaches. As describedpreviously, the present algorithm is dynamic and adjusts itself tovarying light conditions. In addition, the use of the present inventiondoes not depend on the database and the typical characteristics of theimages in the database, unlike the approach of Wildes et al. or Cannyedge detection where thresholds must be specified according to thequality of the images in the particular database.

Moreover, a notable advantage to Otsu's method is that the thresholdsare not selected according to differentiation but according tointegration of the histogram. Contrary to the differentiation approachof Daugman which looks for a local property, i.e. a change in luminance,Otsu's method is a global approach based on the grey level histogramreflecting the entire image. Thus, the present invention is notsensitive to localized noise or imperfections in the image. Forinstance, edge based algorithms fail when edges from a contact lens arepresent in the image. Such algorithms cannot differentiate between theedge of a contact lens and the boundary of the iris. The presentinvention ignores the presence of edges in favor of detecting grey scaleobjects which correspond to parts of the eye.

Although the multi-level thresholding of step 106 employs a thresholdaimed at identifying the pupil image, the thresholded pupil image maycontain pixels which do not represent the pupil image. As illustrated inFIG. 5B, extraneous parts of the image, such as parts of the eyelash andeyelid images, may fall within the threshold that corresponds with thepupil and become a part of the thresholded image. Therefore, referringto FIG. 1, a process such as grey level connected component analysis isapplied, in step 110, to determine the pixels corresponding to the pupilimage and separate a clean shape for the pupil from these extraneousparts.

Once the thresholded pupil image is processed using connected componentanalysis, a simple min-max method can be applied to the area, in step112, to identify the center of the pupil and the size of the pupil.Alternatively, a circular or elliptical Hough transform can also beapplied to get pupil's size and center.

The pupil aspect and size are evaluated in step 114. In particular, theedges may be tested for circularity. For instance, circularity may betested by applying Freeman chain coding analysis, as described by H.Freeman in “Boundary encoding and processing,” Picture Processing andPsychopictorics, B. Lipkin and A. Rosenfeld, eds., Academic Press, Inc,New York, 1970, pp. 241-266. If the thresholded pupil image appears tohave the proper aspect and size, the process proceeds to step 122. Inmost cases, improper pupil aspect and size occur when the thresholdedpupil image also includes an iris image, which causes the calculatedsize to be too large. This may occur if the grey level values for thepixels corresponding to the pupil and the iris are too close to enablethe multi-level thresholding step 106 to separate the iris image fromthe pupil image in the thresholded pupil image. As a result, thethresholded pupil image must be further processed in step 116 to segmentthe pupil and iris images.

In particular, step 116 applies bimodal thresholding to the thresholdedpupil image. The steps illustrated in FIG. 3 are applied in step 116,using only two classes—one for each of the pupil image and the irisimage—unlike the step 106 where more than two classes are preferablyused. As shown in step 302, a pupil histogram 304 of pixel intensitiesis created from a distribution of grey levels in the thresholded pupilimage. In step 306, the modified between-class variances according tothe method of Liao et al. are calculated and stored in a lookup table308 to make arithmetic operations more efficient. In step 310, themodified between-class variance is maximized, and in step 312, thethresholds 314 corresponding to the two classes in the eye histogram 304are determined. Thus, the pupil and iris images of the eye image areidentified according to the calculated thresholds 314. Because theinitial thresholded pupil image 108 in this case contains both a pupilimage and iris image, applying bimodal thresholding to the thresholdedpupil image yields a new thresholded pupil image 118 in addition to anew thresholded iris image 119.

Referring again to FIG. 1, the thresholded pupil image 118 from step 116may be processed again in step 120 with grey level connected componentanalysis. Meanwhile, the thresholded iris image 119 serves as input tostep 132, which is discussed in further detail below. It should be notedthat if the thresholded pupil image 108 includes pixels that correspondto the iris image, these pixels will not be included thresholded irisimage 130 that result from the multi-level thresholding step 106. As aresult, the thresholded iris image 119 is necessary for processing ofthe iris image.

As described above, the connected component analysis in step 120 allowspixels representing non-pupil components in the thresholded image to beseparated from pixels representing the pupil, further separating a cleanshape for the pupil from any extraneous parts.

The thresholded pupil image is evaluated again in step 122 to determinewhether the size and aspect of the thresholded pupil image falls withinan acceptable range. As described above, the edges may be tested forcircularity with a technique such as Freeman chain coding analysis. Ifthe size and aspect of the image are acceptable, the size and positionof the thresholded pupil image are verified and refined, in step 124.Otherwise, a “pupil error” is declared in step 126, and the capturedimage cannot be used to obtain biometric data.

In step 124, verifying and refining the size and position of thethresholded pupil image may employ an integro-differential method or theHough circle method. As described previously, with anintegro-differential method, the center and boundary of the pupil may bedetermined by searching a three-parameter space for the best combinationof center coordinates (x₀, y₀) and radius r, i.e., where the absolutevalue of the partial derivative with respect to radius r of theintegrated luminance along the circle is maximum. As also describedpreviously, with the Hough circle method, the pupil is modeled as acircle and the edge pixels are thinned and histogrammed into (x₀, y₀, r)values, where the (x₀, y₀, r) point with the most votes are taken torepresent the pupillary boundary. Indeed, the current invention can beused as a preprocessing step for conventional methods, such as thosetaught by Daugman, which employs an integro-differential method, orWildes et al., which employs the Hough circle method. Employing theglobal thresholding technique of the present invention, however,eliminates the need to search a large parameter space as currentlyrequired by the conventional methods that rely solely on anintegro-differential method or the Hough circle method.

Alternatively, the step 124 can verify and refine the size and positionof the pupil by employing a caliper tool to detect edges, although thecaliper cannot be used when no approximate location is known. Anotherpossibility for the step 124 includes employing a hill-climbing methodon the edges.

The verification and refining process of step 124 produces a pupil sizeand position. If a result cannot be determined or if the result is notreasonable, a “pupil error” is declared in step 126, and the capturedimage cannot be used to obtain biometric data. Otherwise, the pupil sizeand position 128 are used in further processing. In particular, theouter boundary of the thresholded pupil image is used to determine theinner boundary of iris image.

As described previously with reference to FIG. 1, the multi-levelthresholding in step 106 also produces a thresholded iris image 130corresponding with the iris image. In step 132, the outer boundary ofthe iris image is determined by enhancing the edges of the thresholdediris image 130. As discussed previously, if the multi-level thresholdingstep 106 does not separate the pupil and iris images and further bimodalthresholding is required in step 116, the thresholded iris image 119,which results from step 116, is used as input for step 132 in place ofthresholded image 130.

Because a thresholded iris image 130 or 119 is available, there is noneed to use a computationally expensive edge operator for edgeenhancement in step 132. Rather a very simple edge tool, such as theSobel edge detector, can be used. Specifically, the Sobel edge detectoris used with angle filtering which restricts the edges to those thatcorrespond with the center of the pupil. Other possible edge tools, suchas Canny, Shen-Casten, or Spacek edge detectors, can be used, but aregenerally more computationally expensive.

In step 134, the shape of the iris image is determined from thethresholded iris image with enhanced edges. Determining the shape of theiris may include employing one, or a combination of, anintegro-differential technique for finding a circle or ellipse, a Houghmethod for finding a circle or ellipse, and a least squares fit ofpoints to a circle or ellipse.

In step 136, the size and position of the iris image is verified andrefined using the same approaches for the processing of the pupil imagein step 124. In relation to the verification and refinement of step 136,the shape finder of step 134 can be seen as a “coarse find.”

The verification and refining process of step 136 produces an iris sizeand position. If a result cannot be determined, an “iris error” isdeclared in step 138, and the captured image cannot be used to obtainbiometric data. Otherwise, the thresholded iris image proceeds to thenext step.

As shown in FIG. 2, the eye image 200 also includes an eyelids image.The eyelids image 212 forms a part of the outer boundary 210 of the irisimage 206. Although the iris image may be occluded by eyelids oreyelashes, the present invention produces a clean shape usingthresholding, which allows any edge extraction method to work. Once theedges have been identified, the boundary of the iris image may be foundby a circular or elliptical Hough transform or, alternatively, acircular or elliptical hill-climbing fit routine. Thus, the presentinvention avoids the problem of isolating too much of the iris that canoccur with Masek's method. Any pixels of the eye image which falloutside the calculated boundary of the iris image can be disregarded asanother eye component, such an eyelid.

Once the eyelids and eyelashes are detected and eliminated in step 140using the calculated outer boundary of the iris image, information aboutthe iris size and position 142 can be applied to the original eye imageto obtain biometric data from the iris.

Preferably, there is even-lighting across the eye when the image istaken so that the histogram is not affected by the pixels that are apart of the same eye component but which show different grey scales dueto the different amounts of light they receive. For example, lightreflecting off the nose can make one side of the eye brighter and affectanalysis of the image. Such uneven lighting can be corrected bybackground analysis. Even with uneven lighting, the approximatepositions of the pupil and iris can be identified. Therefore, thepresent invention may use optional shading correction to improveaccuracy.

Accordingly, using these approximate positions, pixel values can besampled from each side of the sclera to determine the level of unevenillumination and to make appropriate corrections according to knownbackground analysis techniques (multiplicative background analysis orbackground subtraction method). For instance, grey scale values can befitted between the left and right sample pixels from the sclera andapplied to the entire image. The grey level average of the left andright regions of the sclera provides an estimate of the shading.

As illustrated in step 602 in an exemplary corrective method 600 in FIG.6, sample grey level values 604 are sampled from the left half and theright half of the sclera image. In step 606, the difference between theleft and right grey level values are determined. As indicated in step610, if the shading estimate is found to be larger than a set thresholdin step 608, the two grey scale values can be used to obtain a shadingdifference that can be subtracted from the eye image to get a moreevenly illuminated eye image 612. Once the uneven lighting effect iscorrected, image segmentation according to the present invention can berepeated in step 614 to achieve better results for locating the iris.

In addition, the present invention is not affected by the presence of alight spot reflection, which can confuse edge based algorithms which arelooking for the type of brightness change or edge created by the spotreflection. A light spot reflection is insignificant when the grey scalefor a larger (pupillary) area is considered. That is, usually the spotlighting will have much higher grey scale than that of the pupil. As aresult, the thresholded image for the pupil excludes the light spot.

Furthermore, because the present invention uses grey scale information,it can be used to locate pupil or iris even when the edges becomeblurred, especially by motion.

The processing steps of the present invention may be implemented on oneor more computer systems or other electronic programmable devices,programmed according to the teachings of the exemplary embodiments ofthe present invention. In addition, the present invention may employcomputer readable media or memories for holding instructions programmedaccording to the teachings of the present inventions and for holdingdata structures, tables, records, and/or other data described herein.

While various embodiments in accordance with the present invention havebeen shown and described, it is understood that the invention is notlimited thereto. The present invention may be changed, modified andfurther applied by those skilled in the art. Therefore, this inventionis not limited to the detail shown and described previously, but alsoincludes all such changes and modifications.

What is claimed is:
 1. A computer-implemented method for segmentingcomponents of an eye image for creating data to be used in a system forprocessing eye image data, the method executed by one or more computingsystems and comprising: creating, by the one or more computing systems,an eye histogram of pixel intensities from a distribution of grey levelsof an eye image, the eye histogram having two or more classes, eachclass corresponding to a component of the eye image; selecting, by theone or more computing systems, thresholds in the eye histogram to dividethe classes of the histogram; creating, by the one or more computingsystems, threshold images, each of the threshold images corresponding toone of the two or more classes; and removing, by the one or morecomputing systems, a portion of the eye image based on one or more ofthe threshold images.
 2. The method of claim 1, wherein one of thethreshold images corresponds to an eyelash image, and removing a portionof the eye image based on one or more of the threshold images comprisesremoving a portion of the eye image based on the threshold imagecorresponding to the eyelash image.
 3. The method of claim 1, whereinone of the threshold images corresponds to a specular reflection image,and removing a portion of the eye image based on one or more of thethreshold images comprises removing a portion of the eye image based onthe threshold image corresponding to the specular reflection image. 4.The method of claim 1, wherein selecting thresholds in the eye histogramcomprises determining maximum between-class variances between the two ormore classes.
 5. The method of claim 4, wherein determining the maximumbetween-class variance between the two or more the classes comprisesretrieving results for arithmetic calculations from a lookup table.
 6. Asystem comprising: one or more processors; and a computer-readablemedium coupled to at least one of the one or more processors and havinginstructions stored thereon which, when executed by the at least one ofthe one or more processors, causes at least one of the one or moreprocessors to perform operations comprising: creating an eye histogramof pixel intensities from a distribution of grey levels of an eye image,the eye histogram having two or more classes, each class correspondingto a component of the eye image; selecting thresholds in the eyehistogram to divide the classes of the histogram; creating thresholdimages, each of the threshold images corresponding to one of the two ormore classes; and removing a portion of the eye image based on one ormore of the threshold images.
 7. The system of claim 6, wherein one ofthe threshold images corresponds to an eyelash image, and removing aportion of the eye image based on one or more of the threshold imagescomprises removing a portion of the eye image based on the thresholdimage corresponding to the eyelash image.
 8. The system of claim 6,wherein one of the threshold images corresponds to a specular reflectionimage, and removing a portion of the eye image based on one or more ofthe threshold images comprises removing a portion of the eye image basedon the threshold image corresponding to the specular reflection image.9. The system of claim 6, wherein selecting thresholds in the eyehistogram comprises determining maximum between-class variances betweenthe two or more classes.
 10. The system of claim 9, wherein determiningthe maximum between-class variance between the two or more the classescomprises retrieving results for arithmetic calculations from a lookuptable.